Robotics Data Pipeline Intern

Robotics Data Pipeline Intern – Multimodal Data

About Us At Persona, we’re building the next generation of humanoid robots, and that requires an unprecedented volume of high-quality, multimodal data. We’re moving beyond basic teleoperation to leverage massive datasets of in-the-wild egocentric video combined with dense sensor streams (IMU, haptics, kinematics, and high-fidelity force profiles). We’re looking for a curious, technically sharp intern to roll up their sleeves and help us turn raw, unstructured multimodal data into high-fidelity training assets for our robots.

The Role As a Data Pipeline Intern, you’ll work directly alongside our data and robotics engineering teams to support the infrastructure that feeds our foundation models. You’ll get hands-on experience with real multimodal data challenges, from sensor stream processing and video pipeline optimization to force analysis and kinematic retargeting. This is not a “fetch coffee and shadow engineers” internship. You’ll own real work and ship real code.

What You’ll Work On

Rebuilding and extending pipelines that ingest and synchronously process egocentric video alongside rich sensor streams (IMU, force-torque, tactile, proprioception)

Owning post-processing algorithms for force analysis and hidden state inference, including contact force estimation, occlusion handling, and inverse kinematics gap-filling

Bridging kinematic retargeting work that translates human hand tracking into humanoid end-effector coordinates

Optimizing and testing data augmentation strategies (spatial, temporal, synthetic viewpoints, sensor noise injection)

Tying together work across our Hardware Teleoperation Team to help align human-robot play-data across modalities

What We’re Looking For

Currently pursuing a B.S., M.S., or Ph.D. in Computer Science, Data Engineering, Machine Learning, Robotics, or a related field

Solid Python skills and exposure to PyTorch, particularly around data loading or multimodal datasets

Coursework or project experience with computer vision, time-series data, or sensor processing

Familiarity with video processing tools (OpenCV, FFmpeg) or pose estimation frameworks (MediaPipe) is a plus

Awareness of imitation learning, VLA architectures, or human-to-robot transfer concepts is a plus, but genuine curiosity counts for a lot here

Bonus Points

Experience with NVIDIA’s robotics stack (Isaac, Cosmos, GR00T)

Exposure to distributed computing (Ray, Spark) or simulation environments (Omniverse, MuJoCo)

Any project work involving synthetic data generation or tactile/spatial data representations